Overview

Dataset statistics

Number of variables12
Number of observations3584
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory336.1 KiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical3

Warnings

updated_dates has a high cardinality: 512 distinct values High cardinality
df_index is highly correlated with latitude and 4 other fieldsHigh correlation
latitude is highly correlated with df_index and 2 other fieldsHigh correlation
longitude is highly correlated with latitudeHigh correlation
confirmed is highly correlated with df_index and 3 other fieldsHigh correlation
deaths is highly correlated with df_index and 2 other fieldsHigh correlation
recovered is highly correlated with df_index and 3 other fieldsHigh correlation
active is highly correlated with df_index and 1 other fieldsHigh correlation
df_index is highly correlated with latitude and 5 other fieldsHigh correlation
year is highly correlated with incidentHigh correlation
latitude is highly correlated with df_index and 2 other fieldsHigh correlation
longitude is highly correlated with df_index and 2 other fieldsHigh correlation
confirmed is highly correlated with df_index and 5 other fieldsHigh correlation
deaths is highly correlated with df_index and 3 other fieldsHigh correlation
recovered is highly correlated with df_index and 3 other fieldsHigh correlation
active is highly correlated with df_index and 3 other fieldsHigh correlation
incident is highly correlated with yearHigh correlation
df_index is highly correlated with latitude and 4 other fieldsHigh correlation
latitude is highly correlated with df_index and 1 other fieldsHigh correlation
longitude is highly correlated with latitudeHigh correlation
confirmed is highly correlated with df_index and 3 other fieldsHigh correlation
deaths is highly correlated with df_index and 3 other fieldsHigh correlation
recovered is highly correlated with df_index and 3 other fieldsHigh correlation
active is highly correlated with df_index and 3 other fieldsHigh correlation
active is highly correlated with recovered and 7 other fieldsHigh correlation
recovered is highly correlated with active and 9 other fieldsHigh correlation
df_index is highly correlated with active and 8 other fieldsHigh correlation
fatality is highly correlated with active and 9 other fieldsHigh correlation
confirmed is highly correlated with active and 8 other fieldsHigh correlation
longitude is highly correlated with active and 8 other fieldsHigh correlation
year is highly correlated with recovered and 2 other fieldsHigh correlation
latitude is highly correlated with active and 8 other fieldsHigh correlation
incident is highly correlated with recovered and 6 other fieldsHigh correlation
deaths is highly correlated with active and 8 other fieldsHigh correlation
Province is highly correlated with active and 8 other fieldsHigh correlation
df_index is uniformly distributed Uniform
Province is uniformly distributed Uniform
updated_dates is uniformly distributed Uniform
df_index has unique values Unique

Reproduction

Analysis started2021-11-07 17:25:33.536969
Analysis finished2021-11-07 17:26:00.670306
Duration27.13 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct3584
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1791.5
Minimum0
Maximum3583
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2021-11-07T22:56:00.824062image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile179.15
Q1895.75
median1791.5
Q32687.25
95-th percentile3403.85
Maximum3583
Range3583
Interquartile range (IQR)1791.5

Descriptive statistics

Standard deviation1034.75601
Coefficient of variation (CV)0.5775919676
Kurtosis-1.2
Mean1791.5
Median Absolute Deviation (MAD)896
Skewness0
Sum6420736
Variance1070720
MonotonicityStrictly increasing
2021-11-07T22:56:01.080398image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
11
 
< 0.1%
23821
 
< 0.1%
23831
 
< 0.1%
23841
 
< 0.1%
23851
 
< 0.1%
23861
 
< 0.1%
23871
 
< 0.1%
23881
 
< 0.1%
23891
 
< 0.1%
Other values (3574)3574
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
35831
< 0.1%
35821
< 0.1%
35811
< 0.1%
35801
< 0.1%
35791
< 0.1%
35781
< 0.1%
35771
< 0.1%
35761
< 0.1%
35751
< 0.1%
35741
< 0.1%

year
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size28.1 KiB
2021
2156 
2020
1428 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters14336
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
20212156
60.2%
20201428
39.8%

Length

2021-11-07T22:56:01.576422image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-07T22:56:01.681599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
20212156
60.2%
20201428
39.8%

Most occurring characters

ValueCountFrequency (%)
27168
50.0%
05012
35.0%
12156
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number14336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
27168
50.0%
05012
35.0%
12156
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Common14336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
27168
50.0%
05012
35.0%
12156
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII14336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27168
50.0%
05012
35.0%
12156
 
15.0%

Province
Categorical

HIGH CORRELATION
UNIFORM

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size28.1 KiB
Sindh
512 
Punjab
512 
Islamabad
512 
Khyber Pakhtunkhwa
512 
Balochistan
512 
Other values (2)
1024 

Length

Max length22
Median length11
Mean length12.42857143
Min length5

Characters and Unicode

Total characters44544
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSindh
2nd rowSindh
3rd rowSindh
4th rowSindh
5th rowSindh

Common Values

ValueCountFrequency (%)
Sindh512
14.3%
Punjab512
14.3%
Islamabad512
14.3%
Khyber Pakhtunkhwa512
14.3%
Balochistan512
14.3%
Azad Jammu and Kashmir512
14.3%
Gilgit-Baltistan512
14.3%

Length

2021-11-07T22:56:01.949469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-07T22:56:02.053741image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
sindh512
9.1%
punjab512
9.1%
islamabad512
9.1%
khyber512
9.1%
pakhtunkhwa512
9.1%
balochistan512
9.1%
azad512
9.1%
jammu512
9.1%
and512
9.1%
kashmir512
9.1%

Most occurring characters

ValueCountFrequency (%)
a7168
16.1%
i3072
 
6.9%
n3072
 
6.9%
h3072
 
6.9%
t2560
 
5.7%
d2048
 
4.6%
s2048
 
4.6%
l2048
 
4.6%
m2048
 
4.6%
2048
 
4.6%
Other values (21)15360
34.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter36352
81.6%
Uppercase Letter5632
 
12.6%
Space Separator2048
 
4.6%
Dash Punctuation512
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a7168
19.7%
i3072
 
8.5%
n3072
 
8.5%
h3072
 
8.5%
t2560
 
7.0%
d2048
 
5.6%
s2048
 
5.6%
l2048
 
5.6%
m2048
 
5.6%
u1536
 
4.2%
Other values (11)7680
21.1%
Uppercase Letter
ValueCountFrequency (%)
P1024
18.2%
K1024
18.2%
B1024
18.2%
S512
9.1%
I512
9.1%
A512
9.1%
J512
9.1%
G512
9.1%
Space Separator
ValueCountFrequency (%)
2048
100.0%
Dash Punctuation
ValueCountFrequency (%)
-512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin41984
94.3%
Common2560
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a7168
17.1%
i3072
 
7.3%
n3072
 
7.3%
h3072
 
7.3%
t2560
 
6.1%
d2048
 
4.9%
s2048
 
4.9%
l2048
 
4.9%
m2048
 
4.9%
u1536
 
3.7%
Other values (19)13312
31.7%
Common
ValueCountFrequency (%)
2048
80.0%
-512
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII44544
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a7168
16.1%
i3072
 
6.9%
n3072
 
6.9%
h3072
 
6.9%
t2560
 
5.7%
d2048
 
4.6%
s2048
 
4.6%
l2048
 
4.6%
m2048
 
4.6%
2048
 
4.6%
Other values (21)15360
34.5%

updated_dates
Categorical

HIGH CARDINALITY
UNIFORM

Distinct512
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size28.1 KiB
2021-01-02
 
7
2021-01-03
 
7
2020-07-23
 
7
2020-07-22
 
7
2020-07-21
 
7
Other values (507)
3549 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters35840
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-01-02
2nd row2021-01-03
3rd row2021-01-04
4th row2021-01-05
5th row2021-01-06

Common Values

ValueCountFrequency (%)
2021-01-027
 
0.2%
2021-01-037
 
0.2%
2020-07-237
 
0.2%
2020-07-227
 
0.2%
2020-07-217
 
0.2%
2020-07-207
 
0.2%
2020-07-197
 
0.2%
2020-07-187
 
0.2%
2020-07-177
 
0.2%
2020-07-167
 
0.2%
Other values (502)3514
98.0%

Length

2021-11-07T22:56:02.360493image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-01-027
 
0.2%
2021-01-037
 
0.2%
2020-07-237
 
0.2%
2020-07-227
 
0.2%
2020-07-217
 
0.2%
2020-07-207
 
0.2%
2020-07-197
 
0.2%
2020-07-187
 
0.2%
2020-07-177
 
0.2%
2020-07-167
 
0.2%
Other values (502)3514
98.0%

Most occurring characters

ValueCountFrequency (%)
09520
26.6%
29121
25.4%
-7168
20.0%
15110
14.3%
7784
 
2.2%
8784
 
2.2%
9763
 
2.1%
3756
 
2.1%
6700
 
2.0%
4567
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28672
80.0%
Dash Punctuation7168
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09520
33.2%
29121
31.8%
15110
17.8%
7784
 
2.7%
8784
 
2.7%
9763
 
2.7%
3756
 
2.6%
6700
 
2.4%
4567
 
2.0%
5567
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
-7168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common35840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09520
26.6%
29121
25.4%
-7168
20.0%
15110
14.3%
7784
 
2.2%
8784
 
2.2%
9763
 
2.1%
3756
 
2.1%
6700
 
2.0%
4567
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII35840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09520
26.6%
29121
25.4%
-7168
20.0%
15110
14.3%
7784
 
2.2%
8784
 
2.2%
9763
 
2.1%
3756
 
2.1%
6700
 
2.0%
4567
 
1.6%

latitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.87417857
Minimum26.009446
Maximum35.792146
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2021-11-07T22:56:02.459964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum26.009446
5-th percentile26.009446
Q128.328492
median33.665087
Q334.485332
95-th percentile35.792146
Maximum35.792146
Range9.7827
Interquartile range (IQR)6.15684

Descriptive statistics

Standard deviation3.340887503
Coefficient of variation (CV)0.1048148581
Kurtosis-1.099548455
Mean31.87417857
Median Absolute Deviation (MAD)2.127059
Skewness-0.5897898054
Sum114237.056
Variance11.16152931
MonotonicityNot monotonic
2021-11-07T22:56:02.756331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
26.009446512
14.3%
30.811346512
14.3%
33.665087512
14.3%
34.485332512
14.3%
28.328492512
14.3%
34.027401512
14.3%
35.792146512
14.3%
ValueCountFrequency (%)
26.009446512
14.3%
28.328492512
14.3%
30.811346512
14.3%
33.665087512
14.3%
34.027401512
14.3%
34.485332512
14.3%
35.792146512
14.3%
ValueCountFrequency (%)
35.792146512
14.3%
34.485332512
14.3%
34.027401512
14.3%
33.665087512
14.3%
30.811346512
14.3%
28.328492512
14.3%
26.009446512
14.3%

longitude
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.56523457
Minimum65.898403
Maximum74.982138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2021-11-07T22:56:02.859692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum65.898403
5-th percentile65.898403
Q168.776807
median72.139132
Q373.947253
95-th percentile74.982138
Maximum74.982138
Range9.083735
Interquartile range (IQR)5.170446

Descriptive statistics

Standard deviation2.934571521
Coefficient of variation (CV)0.04100554604
Kurtosis-0.5585674741
Mean71.56523457
Median Absolute Deviation (MAD)1.808121
Skewness-0.8268508823
Sum256489.8007
Variance8.611710011
MonotonicityNot monotonic
2021-11-07T22:56:02.955204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
68.776807512
14.3%
73.121219512
14.3%
72.09169512
14.3%
65.898403512
14.3%
73.947253512
14.3%
74.982138512
14.3%
72.139132498
13.9%
72.13913214
 
0.4%
ValueCountFrequency (%)
65.898403512
14.3%
68.776807512
14.3%
72.09169512
14.3%
72.139132498
13.9%
72.13913214
 
0.4%
73.121219512
14.3%
73.947253512
14.3%
74.982138512
14.3%
ValueCountFrequency (%)
74.982138512
14.3%
73.947253512
14.3%
73.121219512
14.3%
72.13913214
 
0.4%
72.139132498
13.9%
72.09169512
14.3%
68.776807512
14.3%
65.898403512
14.3%

confirmed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3396
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94213.90765
Minimum444
Maximum470978
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2021-11-07T22:56:03.095443image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum444
5-th percentile2613
Q112516.25
median35209
Q3131801.25
95-th percentile372601.8
Maximum470978
Range470534
Interquartile range (IQR)119285

Descriptive statistics

Standard deviation118279.6815
Coefficient of variation (CV)1.255437594
Kurtosis1.670154145
Mean94213.90765
Median Absolute Deviation (MAD)30797
Skewness1.613536579
Sum337662645
Variance1.399008306 × 1010
MonotonicityNot monotonic
2021-11-07T22:56:03.254281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48826
 
0.2%
49596
 
0.2%
49566
 
0.2%
103904
 
0.1%
49514
 
0.1%
332203
 
0.1%
342533
 
0.1%
21053
 
0.1%
28163
 
0.1%
49093
 
0.1%
Other values (3386)3543
98.9%
ValueCountFrequency (%)
4441
< 0.1%
5342
0.1%
5741
< 0.1%
6471
< 0.1%
6631
< 0.1%
7031
< 0.1%
7401
< 0.1%
7691
< 0.1%
8031
< 0.1%
8131
< 0.1%
ValueCountFrequency (%)
4709781
< 0.1%
4706901
< 0.1%
4704211
< 0.1%
4701751
< 0.1%
4699601
< 0.1%
4694751
< 0.1%
4691221
< 0.1%
4687761
< 0.1%
4684011
< 0.1%
4681641
< 0.1%

deaths
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1970
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2070.7726
Minimum9
Maximum12936
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2021-11-07T22:56:03.418184image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile66
Q1175
median681.5
Q32829.75
95-th percentile9019.85
Maximum12936
Range12927
Interquartile range (IQR)2654.75

Descriptive statistics

Standard deviation2894.014597
Coefficient of variation (CV)1.397553066
Kurtosis3.39571609
Mean2070.7726
Median Absolute Deviation (MAD)579.5
Skewness1.935487177
Sum7421649
Variance8375320.489
MonotonicityNot monotonic
2021-11-07T22:56:03.585488image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10250
 
1.4%
10743
 
1.2%
10340
 
1.1%
18636
 
1.0%
10127
 
0.8%
17524
 
0.7%
14524
 
0.7%
74022
 
0.6%
11121
 
0.6%
18218
 
0.5%
Other values (1960)3279
91.5%
ValueCountFrequency (%)
91
 
< 0.1%
102
0.1%
111
 
< 0.1%
133
0.1%
141
 
< 0.1%
154
0.1%
162
0.1%
173
0.1%
182
0.1%
191
 
< 0.1%
ValueCountFrequency (%)
129361
< 0.1%
129291
< 0.1%
129241
< 0.1%
129181
< 0.1%
129152
0.1%
129091
< 0.1%
129041
< 0.1%
129021
< 0.1%
128981
< 0.1%
128961
< 0.1%

recovered
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2711
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69822.32576
Minimum217
Maximum339379
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2021-11-07T22:56:03.753895image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum217
5-th percentile2243.4
Q112377
median47167.5
Q381523.5
95-th percentile267407.15
Maximum339379
Range339162
Interquartile range (IQR)69146.5

Descriptive statistics

Standard deviation79608.88173
Coefficient of variation (CV)1.140163707
Kurtosis2.531833401
Mean69822.32576
Median Absolute Deviation (MAD)34695.5
Skewness1.743747971
Sum250243215.5
Variance6337574050
MonotonicityNot monotonic
2021-11-07T22:56:04.019037image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69822.32576637
 
17.8%
485613
 
0.4%
118616
 
0.2%
20025
 
0.1%
22125
 
0.1%
48674
 
0.1%
47874
 
0.1%
48534
 
0.1%
47954
 
0.1%
48054
 
0.1%
Other values (2701)2898
80.9%
ValueCountFrequency (%)
2171
< 0.1%
2372
0.1%
2421
< 0.1%
2541
< 0.1%
2641
< 0.1%
2901
< 0.1%
3021
< 0.1%
3121
< 0.1%
3171
< 0.1%
3361
< 0.1%
ValueCountFrequency (%)
3393791
< 0.1%
3343051
< 0.1%
3338821
< 0.1%
3336501
< 0.1%
3335291
< 0.1%
3332011
< 0.1%
3331981
< 0.1%
3328301
< 0.1%
3327771
< 0.1%
3324291
< 0.1%

active
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2237
Distinct (%)62.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5935.375976
Minimum0
Maximum49980
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2021-11-07T22:56:04.252270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile105.15
Q1664.75
median3363.5
Q35935.375976
95-th percentile23130.1
Maximum49980
Range49980
Interquartile range (IQR)5270.625976

Descriptive statistics

Standard deviation8359.487916
Coefficient of variation (CV)1.408417588
Kurtosis8.329876326
Mean5935.375976
Median Absolute Deviation (MAD)2588
Skewness2.718163508
Sum21272387.5
Variance69881038.22
MonotonicityNot monotonic
2021-11-07T22:56:04.433416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5935.375976637
 
17.8%
3429
 
0.3%
3338
 
0.2%
1148
 
0.2%
3597
 
0.2%
196
 
0.2%
2956
 
0.2%
1186
 
0.2%
1086
 
0.2%
3806
 
0.2%
Other values (2227)2885
80.5%
ValueCountFrequency (%)
02
0.1%
12
0.1%
22
0.1%
31
 
< 0.1%
44
0.1%
52
0.1%
62
0.1%
82
0.1%
92
0.1%
111
 
< 0.1%
ValueCountFrequency (%)
499801
< 0.1%
498841
< 0.1%
493491
< 0.1%
493271
< 0.1%
492411
< 0.1%
484231
< 0.1%
480851
< 0.1%
480031
< 0.1%
476901
< 0.1%
476321
< 0.1%

incident
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct3399
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean669.9504665
Minimum10.97552113
Maximum5333.573876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2021-11-07T22:56:04.651244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum10.97552113
5-th percentile76.07609602
Q1152.2592254
median314.0531141
Q3608.4977425
95-th percentile3883.762955
Maximum5333.573876
Range5322.598355
Interquartile range (IQR)456.2385171

Descriptive statistics

Standard deviation1046.790887
Coefficient of variation (CV)1.562489974
Kurtosis8.881420128
Mean669.9504665
Median Absolute Deviation (MAD)192.5196925
Skewness3.055448302
Sum2401102.472
Variance1095771.162
MonotonicityNot monotonic
2021-11-07T22:56:04.836242image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
481.6571696
 
0.2%
489.2539746
 
0.2%
488.95799466
 
0.2%
1025.0753764
 
0.1%
488.46469564
 
0.1%
487.37943773
 
0.1%
484.32098383
 
0.1%
313.41818592
 
0.1%
3885.9308312
 
0.1%
108.56737722
 
0.1%
Other values (3389)3546
98.9%
ValueCountFrequency (%)
10.975521131
< 0.1%
13.200288922
0.1%
14.189074611
< 0.1%
15.993608491
< 0.1%
16.389122761
< 0.1%
17.377908451
< 0.1%
18.292535211
< 0.1%
19.009404841
< 0.1%
19.849872671
< 0.1%
20.097069091
< 0.1%
ValueCountFrequency (%)
5333.5738761
< 0.1%
5331.9791171
< 0.1%
5329.7364861
< 0.1%
5328.5404161
< 0.1%
5327.294511
< 0.1%
5326.1981131
< 0.1%
5324.25451
< 0.1%
5323.1581021
< 0.1%
5321.3639981
< 0.1%
5319.9685831
< 0.1%

fatality
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3397
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.026813803
Minimum0.8620606061
Maximum4.19907758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.1 KiB
2021-11-07T22:56:05.115826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.8620606061
5-th percentile0.9360269847
Q11.126190772
median2.029292606
Q32.804357597
95-th percentile3.198653441
Maximum4.19907758
Range3.337016974
Interquartile range (IQR)1.678166824

Descriptive statistics

Standard deviation0.798536003
Coefficient of variation (CV)0.3939858718
Kurtosis-1.260088614
Mean2.026813803
Median Absolute Deviation (MAD)0.8167014447
Skewness0.1134852481
Sum7264.100668
Variance0.6376597481
MonotonicityNot monotonic
2021-11-07T22:56:05.507499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.058111386
 
0.2%
2.0688242526
 
0.2%
2.0601898614
 
0.1%
1.7901828684
 
0.1%
2.0778162563
 
0.1%
2.7472527473
 
0.1%
2.0647773283
 
0.1%
2.0568663043
 
0.1%
1.1224074832
 
0.1%
0.90799497272
 
0.1%
Other values (3387)3548
99.0%
ValueCountFrequency (%)
0.86206060611
< 0.1%
0.86239364941
< 0.1%
0.86290011571
< 0.1%
0.86311861741
< 0.1%
0.86356287451
< 0.1%
0.86361398331
< 0.1%
0.8642563411
< 0.1%
0.86437919641
< 0.1%
0.86506355671
< 0.1%
0.86529421921
< 0.1%
ValueCountFrequency (%)
4.199077581
< 0.1%
4.0032938492
0.1%
3.911056961
< 0.1%
3.8494875851
< 0.1%
3.8301456741
< 0.1%
3.8274144651
< 0.1%
3.8258229971
< 0.1%
3.7950937951
< 0.1%
3.767953741
< 0.1%
3.7472936211
< 0.1%

Interactions

2021-11-07T22:55:40.458860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:40.631171image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:40.763190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:41.016226image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:41.332329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:41.781769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:42.141331image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:42.328288image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:42.779836image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:43.210086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:43.500602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:43.670485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:44.038333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:44.343415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:44.469463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:44.745456image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:45.182310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:45.554635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:45.970207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:46.460484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:46.804618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:46.991557image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:47.158310image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:47.740303image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:48.164047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:48.738122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:49.231754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:49.465082image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:49.825669image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:49.945678image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:50.066973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:50.266914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:50.463327image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:50.834657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:51.132688image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:51.333802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:51.549750image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:51.698471image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:51.930524image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:52.042534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:52.153523image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:52.335722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:52.477740image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:52.597007image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:52.857687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:53.211633image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:53.683141image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:53.851139image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:53.971092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:54.257148image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:54.402333image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:54.613340image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:54.748813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:54.870967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:55.142058image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:55.295204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:55.421190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:55.719376image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:55.840388image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:55.953370image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:56.071369image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:56.181728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:56.292126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:56.405127image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:56.522517image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:56.632566image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:56.747565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:56.861583image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:57.094291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:57.389185image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:57.643442image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:57.755461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:57.868492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:57.995459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:58.111459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:58.227584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:58.344693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:58.471695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:58.604415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:58.732445image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-11-07T22:55:59.105063image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-11-07T22:56:05.834511image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-07T22:56:06.197315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-07T22:56:06.576192image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-07T22:56:07.337587image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-11-07T22:56:07.755905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-07T22:55:59.729307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-07T22:56:00.466047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexyearProvinceupdated_dateslatitudelongitudeconfirmeddeathsrecoveredactiveincidentfatality
002021Sindh2021-01-0226.00944668.776807216632.03582.0196134.016916.0452.3906141.653495
112021Sindh2021-01-0326.00944668.776807217636.03594.0196677.017365.0454.4872581.651381
222021Sindh2021-01-0426.00944668.776807218597.03611.0197430.017556.0456.4941051.651898
332021Sindh2021-01-0526.00944668.776807219452.03623.0197870.017959.0458.2795941.650930
442021Sindh2021-01-0626.00944668.776807220501.03634.0198577.018290.0460.4702111.648065
552021Sindh2021-01-0726.00944668.776807221734.03653.0199649.018432.0463.0450731.647469
662021Sindh2021-01-0826.00944668.776807222999.03670.0202034.017295.0465.6867611.645747
772021Sindh2021-01-0926.00944668.776807224004.03679.0202570.017755.0467.7854941.642381
882021Sindh2021-01-1026.00944668.776807225509.03693.0203328.018488.0470.9283711.637629
992021Sindh2021-01-1126.00944668.776807226338.03699.0204075.018564.0472.6595641.634281

Last rows

df_indexyearProvinceupdated_dateslatitudelongitudeconfirmeddeathsrecoveredactiveincidentfatality
357435742020Gilgit-Baltistan2020-12-2235.79214674.9821384831.099.04636.096.0476.6255192.049265
357535752020Gilgit-Baltistan2020-12-2335.79214674.9821384832.099.04639.094.0476.7241792.048841
357635762020Gilgit-Baltistan2020-12-2435.79214674.9821384838.099.04649.090.0477.3161382.046300
357735772020Gilgit-Baltistan2020-12-2535.79214674.9821384844.0101.04671.072.0477.9080962.085054
357835782020Gilgit-Baltistan2020-12-2635.79214674.9821384847.0101.04677.069.0478.2040762.083763
357935792020Gilgit-Baltistan2020-12-2735.79214674.9821384850.0101.04683.066.0478.5000552.082474
358035802020Gilgit-Baltistan2020-12-2835.79214674.9821384850.0101.04686.063.0478.5000552.082474
358135812020Gilgit-Baltistan2020-12-2935.79214674.9821384853.0101.04696.056.0478.7960352.081187
358235822020Gilgit-Baltistan2020-12-3035.79214674.9821384855.0101.04700.054.0478.9933542.080330
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